Deep Learning-Based Projection of Occurrence Frequency of Forest Fires under SSP Scenario: Exploring the Link between Drought Characteristics and Forest Fires

Abstract

The occurrence frequency of forest fires (OF) can be estimated using drought features because droughts are affected by climatic conditions. Previous studies have improved OF estimation performance by applying the meteorological drought index to climatic conditions. It is anticipated that the temperature will rise in South Korea in the future and that drought will become severe on account of climate change. The future OF is expected to change accordingly. This study used the standard precipitation index, relative humidity, and wind speed as predictor variables for a deep-learning-based model to estimate the OF. Climate change scenarios under shared socioeconomic pathways were used to estimate future OF. As a result, it was projected that the OF in the summer season will increase in the future (2071–2100). In particular, there will be a 15% increase in July compared to the current climate. A decrease in relative humidity and increase in wind speed will also affect the OF. Finally, drought severity was found to be the most influential factor on the OF among the four drought characteristics (severity, duration, intensity, and inter-arrival), considering inter-model variability across all global climate models.